On active contour models and balloons
CVGIP: Image Understanding
Watersheds in Digital Spaces: An Efficient Algorithm Based on Immersion Simulations
IEEE Transactions on Pattern Analysis and Machine Intelligence
Morphological multiscale segmentation for image coding
Proceedings of of the IEEE winter workshop on Nonlinear digital signal processing
Watershed of a continuous function
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
Topographic distance and watershed lines
Signal Processing - Special issue on mathematical morphology and its applications to signal processing
International Journal of Computer Vision
The watershed transform: definitions, algorithms and parallelization strategies
Fundamenta Informaticae - Special issue on mathematical morphology
Snakes, shapes, and gradient vector flow
IEEE Transactions on Image Processing
Journal of Mathematical Imaging and Vision
Morphological multiscale decomposition of connected regions with emphasis on cell clusters
Computer Vision and Image Understanding
Cyclic mathematical morphology in polar-logarithmic representation
IEEE Transactions on Image Processing
IWCIA '09 Proceedings of the 13th International Workshop on Combinatorial Image Analysis
Morphological Connected Filtering on Viscous Lattices
Journal of Mathematical Imaging and Vision
Segmentation of 4D cardiac MRI: Automated method based on spatio-temporal watershed cuts
Image and Vision Computing
Simplification of color images using semi-flat morphological operators and statistical metrics
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Overview of adaptive morphology: trends and perspectives
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
General adaptive neighborhood viscous mathematical morphology
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Cytology imaging segmentation using the locally constrained watershed transform
ISMM'11 Proceedings of the 10th international conference on Mathematical morphology and its applications to image and signal processing
Hypercomplex Mathematical Morphology
Journal of Mathematical Imaging and Vision
General Adaptive Neighborhood-Based Pretopological Image Filtering
Journal of Mathematical Imaging and Vision
A noise tolerant watershed transformation with viscous force for seeded image segmentation
ACCV'12 Proceedings of the 11th Asian conference on Computer Vision - Volume Part I
Hi-index | 0.00 |
The watershed transform is the basic morphological tool for image segmentation. Watershed lines, also called divide lines, are a topographical concept: a drop of water falling on a topographical surface follows a steepest descent line until it stops when reaching a regional minimum. Falling on a divide line, the same drop of water may glide towards one or the other of both adjacent catchment basins. For segmenting an image, one takes as topographic surface the modulus of its gradient: the associated watershed lines will follow the contour lines in the initial image. The trajectory of a drop of water is disturbed if the relief is not smooth: it is undefined for instance on plateaus. On the other hand, each regional minimum of the gradient image is the attraction point of a catchment basin. As gradient images generally present many minima, the result is a strong oversegmentation. For these reasons a more robust scheme is used for the construction of the watershed based on flooding: a set of sources are defined, pouring water in such a way that the altitude of the water increases with constant speed. As the flooding proceeds, the boundaries of the lakes propagate in the direction of the steepest descent line of the gradient. The set of points where lakes created by two distinct sources meet are the contours. As the sources are far less numerous than the minima, there is no more oversegmentation. And on the plateaus the flooding also is well defined and propagates from the boundary towards the inside of the plateau. Used in conjunction with markers, the watershed is a powerful, fast and robust segmentation method. Powerful: it has been used with success in a variety of applications. Robust: it is insensitive to the precise placement or shape of the markers. Fast: efficient algorithms are able to mimic the progression of the flood. In some cases however the resulting segmentation will be poor: the contours always belong to the watershed lines of the gradient and these lines are poorly defined when the initial image is blurred or extremely noisy. In such cases, an additional regularization has to take place. Denoising and filtering the image before constructing the gradient is a widely used method. It is however not always sufficient. In some cases, one desires smoothing the contour, despite the chaotic fluctuations of the watershed lines. For this two options are possible. The first consists in using a viscous fluid for the flooding: a viscous fluid will not be able to follow all irregularities of the relief and produce lakes with smooth boundaries. Simulating a viscous fluid is however computationally intensive. For this reason we propose an alternative solution, in which the topographic surface is modified in such a way that flooding it with a non viscous fluid will produce the same lakes as flooding the original relief with a viscous fluid. On this new relief, the standard watershed algorithm can be used, which has been optimized for various architectures. Two types of viscous fluids will be presented, yielding two distinct regularization methods. We will illustrate the method on various examples.